Spencer Lin

Spencer Lin, Miru Jun, Basem Rizk et al.

This case study presents our user-centered design model for Socially Intelligent Agent (SIA) development frameworks through our experience developing Estuary, an open source multimodal framework for building low-latency real-time socially interactive agents. We leverage the Rapid Assessment Process (RAP) to collect the thoughts of leading researchers in the field of SIAs regarding the current state of the art for SIA development as well as their evaluation of how well Estuary may potentially address current research gaps. We achieve this through a series of end-user interviews conducted by a fellow researcher in the community. We hope that the findings of our work will not only assist the continued development of Estuary but also guide the development of other future frameworks and technologies for SIAs.

Spencer Lin, Basem Rizk, Miru Jun et al.

The rise in capability and ubiquity of generative artificial intelligence (AI) technologies has enabled its application to the field of Socially Interactive Agents (SIAs). Despite rising interest in modern AI-powered components used for real-time SIA research, substantial friction remains due to the absence of a standardized and universal SIA framework. To target this absence, we developed Estuary: a multimodal (text, audio, and soon video) framework which facilitates the development of low-latency, real-time SIAs. Estuary seeks to reduce repeat work between studies and to provide a flexible platform that can be run entirely off-cloud to maximize configurability, controllability, reproducibility of studies, and speed of agent response times. We are able to do this by constructing a robust multimodal framework which incorporates current and future components seamlessly into a modular and interoperable architecture.

Hyundong Cho, Spencer Lin, Tejas Srinivasan et al.

Nonverbal communication (NVC) plays an integral role in human language, but studying NVC in general is challenging because of its broad scope and high variance in interpretation among individuals and cultures. However, mime -- the theatrical technique of suggesting intent using only gesture, expression, and movement -- is a subset of NVC that consists of explicit and embodied actions with much lower human interpretation variance. We argue that a solid understanding of mimed actions is a crucial prerequisite for vision-language models capable of interpreting and commanding more subtle aspects of NVC. Hence, we propose Mime Identification Multimodal Evaluation (MIME), a novel video-based question answering benchmark comprising of 86 mimed actions. Constructed with motion capture data, MIME consists of variations of each action with perturbations applied to the character, background, and viewpoint for evaluating recognition robustness. We find that both open-weight and API-based vision-language models perform significantly worse than humans on MIME, motivating the need for increased research for instilling more robust understanding of human gestures.